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个性化推荐系统中的多样性综述
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Abstract:
多样性已成为推荐系统研究的主要方向之一，提高推荐内容的多样性不仅是解决过度拟合问题的重要方法，也是提高用户体验满意度的方法。为了更好地阐述推荐多样性领域的工作，本文分别从多样性的定义和评价、多样性对推荐质量的影响以及多样化算法本身的发展三个方面对多样性推荐进行了介绍。
Diversity has become one of the main directions of recommendation system research. Improving the diversity of recommendation content is not only an important way to solve the problem of over-fitting, but also a way to improve user’s experience satisfaction. In order to elaborate the work in the field of recommendation diversity, this paper introduces diversity recommendation from three aspects: the definition and evaluation of diversity, the impact of diversity on recommendation quality and the development of diversity algorithm.
| [1] | Salton, G. and McGill, M. (1986) An Introduction to Modern Information Retrieval. McGraw-Hill, New York. |
| [2] | Anhanger, G. and Little, T. (2001) Data Semantics for Improving Retrieval Performance of Digital News Video Systems. IEEE Transactions on Knowledge and Data Engineering, 13, 352-360. https://doi.org/10.1109/69.929894 |
| [3] | Uchygit, G. and Clark, K. (2002) An Agent Based Electronic Program Guide. Proceedings of AH’2002 Workshop Person, Personalization in Future TV, Madrid, May 2002, 46-58. |
| [4] | Kurapati, K., Gutta, S., Schaffer, D., Martino, J. and Zimmerman, J. (2001) A Multi Agent Recommender. Proceedings UM 2001 Workshop Person, Personalization in Future TV, Sonthofen, 13-17 July 2001, 76-84. |
| [5] | Bezzera, B., de Carvalho, F., Ramalho, G. and Zucker, J. (2002) Speeding up Recommender Systems with Meta-Prototypes. In: Advances Artificial Intelligence, Springer Lecture Notes Computer Science, Springer, Berlin, 227-236. https://doi.org/10.1007/3-540-36127-8_22 |
| [6] | Yuan, J., Yu, Y., Xiao, X. and Li, X. (2001) Svm Based Classification Mapping for User Navigation. IEEE Transactions on Knowledge and Data Engineering, 13, 352-360. |
| [7] | Pogacnik, M. (2004) Uporabniku Prilagojeno Iskanje Multimedijskih Vsebin. PhD Thesis, Univ. of Ljubljana, Ljubljana. |
| [8] | Hand, D., Mannila, H. and Smyth, P. (2001) Principles of Data Mining. MIT Press, Cambridge. |
| [9] | Crabtree, B. and Soltysiak, J. (1998) Identifying and Tracking Changing Interests. International Journal on Digital Libraries, 2, 38-53. https://doi.org/10.1007/s007990050035 |
| [10] | Mirkovic, J., Cvetkovic, D., Tomca, N., et al. (1999) Genetic Algorithms for Intelligent Internet Search: A Survey and a Package for Experimenting with Various Locality Types. IEEE TCCA Newsletter, 77-87. |
| [11] | Manjunath, B., Salembier, P. and Sikora, T. (2002) Introduction to MPEG-7 Multimedia Content Description Interface. John Wiley & Sons, Chichester. |
| [12] | Dififino, A., Negro, B. and Chiarotto, A. (2002) A Multi Agent System for a Personalized Electronic Program Guide. Proceedings of AH’2002 Workshop Person, Personalization in Future TV, Madrid, May 2002, 154-161. |
| [13] | Ricci, F., Rokach, L., Shapira, B. and Kantor, P. (2011) Recommender Systems Handbook. Springer, Berlin, 1. |
| [14] | Abbassi, Z., Amer-Yahia, S., Lakshmanan, L., Vassilvitskii, S. and Yu, C. (2009) Getting Recommender Systems to Think outside the Box. Proceedings of the 3rd ACM Conference on Recommender Systems, New York, 23-25 October 2009, 285-288. https://doi.org/10.1145/1639714.1639769 |
| [15] | Fleder, D. and Hosanagar, K. (2007) Recommender Systems and Their Impact on Sales Diversity. Proceedings of the 8th ACM Conference on Electronic Commerce, San Diego, 11-15 June 2007, 192-199. https://doi.org/10.1145/1250910.1250939 |
| [16] | Clarke, C., Kolla, M., Cormack, G., Vechtomova, O., Ashkan, A., Buttcher, S. and MacKinnon, I. (2008) Novelty and Diversity in Information Retrieval Evaluation. International ACM SIGIR Conference on Re-search and Development in Information Retrieval, Singapore, 20-24 July 2008, 659-666. https://doi.org/10.1145/1390334.1390446 |
| [17] | Vargas, S. (2011) New Approaches to Diversity and Novelty in Recommender Systems. 3rd BCS IRSG Symposium on Future Directions in Information Access, Koblenz, 8-13. |
| [18] | Castells, P., Vargas, S. and Wang, J. (2011) Novelty and Diversity Metrics for Recommender Systems: Choice, Discovery and Relevance. Workshop on Diversity in Document Retrieval (DDR 2011) at the 33rd European Conference on Information Retrieval (ECIR 2011), Dublin, 18 April 2011, 29-36. |
| [19] | Hu, R. and Pu, P. (2011) Helping Users Perceive Recommendation Diversity. Workshop on Novelty and Diversity in Recommender Systems, Chicago, 23 October 2011, 43-50. |
| [20] | Vargas, S. (2012) Novelty and Diversity Enhancement and Evaluation in Recommender Systems. Master’s Thesis, Autonomous University of Madrid, Madrid. |
| [21] | Castagnos, S., Brun, A. and Boyer, A. (2013) When Diversity Is Needed...But Not Expected! The 3rd International Conference on Advances in Information Mining and Management, Lisbon, 19-24 November 2013, 44-50. |
| [22] | L’Huillier, A., Castagnos, S. and Boyer, A. (2014) Understanding Usages by Modeling Diversity over Time. 22nd International Conference, UMAP, Aalborg, 7-11 July 2014, 81-86. |
| [23] | Jiang, H., Qi, X. and Sun, H. (2014) Choice-Based Recommender Systems: A Unified Approach to Achieving Relevancy and Diversity. Operations Research, 62, 973-993. https://doi.org/10.1287/opre.2014.1292 |
| [24] | Adomavicius, G. and Kwon, Y. (2012) Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. IEEE Transactions on Knowledge and Data Engineering, 24, 896-911. https://doi.org/10.1109/TKDE.2011.15 |
| [25] | Hurley, N. and Zhang, M. (2011) Novelty and Diversity in Top-n Recommendation Analysis and Evaluation. ACM Transactions on Internet Technology, 10, 14. https://doi.org/10.1145/1944339.1944341 |
| [26] | Ge, M., Gedikli, F. and Jannach, D. (2011) Placing High-Diversity Items in Top-N Recommendation Lists. ITWP 2011, Barcelona, 49-58. |
| [27] | Tintarev, N., Dennis, M. and Masthoff, J. (2013) Adapting Recommen-dation Diversity to Openness to Experience: A Study of Human Behaviour. Proceedings of UMAP, Rome, 10-14 June 2013, 190-202. https://doi.org/10.1007/978-3-642-38844-6_16 |
| [28] | Aytekin, T. and Karakaya, M. (2014) Clustering-Based Diversity Im-provement in Top-N Recommendation. International Journal of Business Information Systems, 42, 1-18. https://doi.org/10.1007/s10844-013-0252-9 |
| [29] | Javari, A. and Jalili, M. (2014) A Probabilistic Model to Resolve Diversi-ty—Accuracy Challenge of Recommendation Systems. Knowledge and Information Systems, 44, 609-627. https://doi.org/10.1007/s10115-014-0779-2 |
| [30] | Ribeiro, M., Lacerda, A., de Moura, E., Hata, I., Veloso, A. and Ziviani, N. (2015) Multi-Objective Pareto-Efficient Approaches for Recommender Systems. ACM Transactions on Intelligent Systems and Technology, 5, Article No. 53. https://doi.org/10.1145/2629350 |
| [31] | Boim, R., Milo, T. and Novgorodov, S. (2011) Diversifi-cation and Refinement in Collaborative Filtering Recommender. CIKM’11, Glasgow, 24-28 October 2011, 739-744. https://doi.org/10.1145/2063576.2063684 |
| [32] | Smyth, B. and McClave, P. (2001) Similarity vs. Diversity. In: Case-Based Reasoning Research and Development, Springer, Berlin, 347-361. https://doi.org/10.1007/3-540-44593-5_25 |
| [33] | Adomavicius, G. and Kwon, Y. (2008) Overcoming Accuracy-Diversity Tradeoff in Recommender Systems: A Variance-Based Approach. 18th Workshop on Information Technology and Systems, Braunschweig, 141-153. |
| [34] | Hurley, N. and Zhang, M. (2011) Novelty and Diversity in Top-N Recommendation—Analysis and Evaluation. ACM Transactions on Internet Technology, 10, 14-21. https://doi.org/10.1145/1944339.1944341 |
| [35] | Pathak, A. and Patra, B.K. (2015) A Knowledge Reuse Framework for Improving Novelty and Diversity in Recommendations. In: Proceedings of the 2nd ACM IKDD Conference on Data Sciences, ACM, New York, 11-19. https://doi.org/10.1145/2732587.2732590 |
| [36] | Vargas, S. (2014) Novelty and Diversity Enhancement and Evaluation in Recommender Systems. International ACM SIGIR Conference on Research, Gold Coast, 6-11 July 2014, 1281-1281. https://doi.org/10.1145/2600428.2610382 |
| [37] | Park, Y.J. (2013) The Adaptive Clustering Method for the Long Tail Problem of Recommender Systems. IEEE Transactions on Knowledge and Data Engineering, 25, 1904-1915. https://doi.org/10.1109/TKDE.2012.119 |
| [38] | Fleder, D. and Hosanagar, K. (2009) Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management Science, 55, 697-712. https://doi.org/10.1287/mnsc.1080.0974 |
| [39] | Adomavicius, G. and Kwon, Y. (2012) Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. IEEE Transactions on Knowledge and Data Engineering, 24, 896-911. https://doi.org/10.1109/TKDE.2011.15 |
| [40] | Basile, P., Musto, C., de Gemmis, M., Lops, P., Narducci, F. and Semeraro, G. (2014) Aggregation Strategies for Linked Open Data-Enabled Recommender Systems. European Semantic Web Conference, Rome, 369-378. |
| [41] | Ho, Y.-C., Chiang, Y.-T. and Hsu, Y.-J. (2014) Who Likes It More? Mining Worth-Recommending Items from Long Tails by Modeling Relative Preference. 7th ACM International Conference on Web Search and Data Mining, New York, 24-28 Feb-ruary 2014, 253-262. |
| [42] | Lee, K. and Lee, K. (2015) Escaping Your Comfort Zone: A Graph-Based Recommender System for Finding Novel Recommendations among Relevant Items. Expert Systems with Applications, 42, 4851-4858. https://doi.org/10.1016/j.eswa.2014.07.024 |
| [43] | Ren, X., Lu, L., Liu, R. and Zhang, J. (2014) Avoiding Congestion in Rec-ommender Systems. New Journal of Physics, 16, Article ID: 063057. https://doi.org/10.1088/1367-2630/16/6/063057 |
| [44] | Bedi, P., Agarwa, S., Singhal, A., Jain, E. and Gupta, G. (2015) A Novel Semantic Clustering Approach for Reasonable Diversity in News Recommendations. The International Conference on “Computational Intelligence in Data Mining”, Odisha, 5-6 December 2015, 437-445. https://doi.org/10.1007/978-81-322-2205-7_41 |
| [45] | di Noia, T., Ostuni, V., Rosati, J., Tomeo, P. and di Sciascio, E. (2014) An Analysis of Users’ Propensity toward Diversity in Recommendations. 8th ACM Conference on Recommender Systems, Foster City, 6-10 October 2014, 285-288. https://doi.org/10.1145/2645710.2645774 |
